Overview

Dataset statistics

Number of variables7
Number of observations2426
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory151.6 KiB
Average record size in memory64.0 B

Variable types

DateTime1
TimeSeries6

Timeseries statistics

Number of series6
Time series length2426
Starting point2010-01-04 00:00:00
Ending point2019-08-26 00:00:00
Period1 day, 10 hours and 50 minutes
2026-02-01T18:37:59.932302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:38:00.167944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Alerts

adj close is highly overall correlated with close and 4 other fieldsHigh correlation
close is highly overall correlated with adj close and 4 other fieldsHigh correlation
high is highly overall correlated with adj close and 4 other fieldsHigh correlation
low is highly overall correlated with adj close and 4 other fieldsHigh correlation
open is highly overall correlated with adj close and 4 other fieldsHigh correlation
volume is highly overall correlated with adj close and 4 other fieldsHigh correlation
adj close is non stationaryNon stationary
close is non stationaryNon stationary
high is non stationaryNon stationary
low is non stationaryNon stationary
open is non stationaryNon stationary
volume is non stationaryNon stationary
adj close is seasonalSeasonal
close is seasonalSeasonal
high is seasonalSeasonal
low is seasonalSeasonal
open is seasonalSeasonal
volume is seasonalSeasonal
Date has unique valuesUnique

Reproduction

Analysis started2026-02-02 00:37:56.861201
Analysis finished2026-02-02 00:37:59.845013
Duration2.98 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

Date
Date

Unique 

Distinct2426
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size37.9 KiB
Minimum2010-01-04 00:00:00
Maximum2019-08-26 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-02-01T18:38:00.298952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:38:00.379537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

adj close
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct2054
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.079246
Minimum26.209999
Maximum113.93
Zeros0
Zeros (%)0.0%
Memory size37.9 KiB
2026-02-01T18:38:00.479337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum26.209999
5-th percentile42.184999
Q152.002501
median73.825001
Q393.879997
95-th percentile104.7
Maximum113.93
Range87.720001
Interquartile range (IQR)41.877496

Descriptive statistics

Standard deviation22.081763
Coefficient of variation (CV)0.3021619
Kurtosis-1.3861088
Mean73.079246
Median Absolute Deviation (MAD)21.08
Skewness-0.029753084
Sum177290.25
Variance487.60428
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.6168924029
2026-02-01T18:38:00.566740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-02-01T18:38:00.797242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps503
min3 days
max5 days
mean3 days, 3 hours and 11 minutes
std8 hours, 18 minutes and 9.91 seconds
2026-02-01T18:38:01.509980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
44.659999856
 
0.2%
53.900001534
 
0.2%
93.959999084
 
0.2%
88.910003663
 
0.1%
100.58999633
 
0.1%
98.669998173
 
0.1%
97.769996643
 
0.1%
97.010002143
 
0.1%
93.879997253
 
0.1%
102.58999633
 
0.1%
Other values (2044)2391
98.6%
ValueCountFrequency (%)
26.209999081
< 0.1%
26.549999241
< 0.1%
27.450000761
< 0.1%
27.940000531
< 0.1%
28.459999081
< 0.1%
29.040000921
< 0.1%
29.420000081
< 0.1%
29.440000531
< 0.1%
29.530000691
< 0.1%
29.639999391
< 0.1%
ValueCountFrequency (%)
113.93000031
< 0.1%
113.51999661
< 0.1%
112.86000061
< 0.1%
112.79000091
< 0.1%
112.76000211
< 0.1%
112.29000091
< 0.1%
112.27999881
< 0.1%
112.20999911
< 0.1%
111.44999691
< 0.1%
111.05000311
< 0.1%
2026-02-01T18:38:00.642318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

close
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct2054
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.079246
Minimum26.209999
Maximum113.93
Zeros0
Zeros (%)0.0%
Memory size37.9 KiB
2026-02-01T18:38:01.954240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum26.209999
5-th percentile42.184999
Q152.002501
median73.825001
Q393.879997
95-th percentile104.7
Maximum113.93
Range87.720001
Interquartile range (IQR)41.877496

Descriptive statistics

Standard deviation22.081763
Coefficient of variation (CV)0.3021619
Kurtosis-1.3861088
Mean73.079246
Median Absolute Deviation (MAD)21.08
Skewness-0.029753084
Sum177290.25
Variance487.60428
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.6168924029
2026-02-01T18:38:02.030405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-02-01T18:38:02.213078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps503
min3 days
max5 days
mean3 days, 3 hours and 11 minutes
std8 hours, 18 minutes and 9.91 seconds
2026-02-01T18:38:02.942745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
44.659999856
 
0.2%
53.900001534
 
0.2%
93.959999084
 
0.2%
88.910003663
 
0.1%
100.58999633
 
0.1%
98.669998173
 
0.1%
97.769996643
 
0.1%
97.010002143
 
0.1%
93.879997253
 
0.1%
102.58999633
 
0.1%
Other values (2044)2391
98.6%
ValueCountFrequency (%)
26.209999081
< 0.1%
26.549999241
< 0.1%
27.450000761
< 0.1%
27.940000531
< 0.1%
28.459999081
< 0.1%
29.040000921
< 0.1%
29.420000081
< 0.1%
29.440000531
< 0.1%
29.530000691
< 0.1%
29.639999391
< 0.1%
ValueCountFrequency (%)
113.93000031
< 0.1%
113.51999661
< 0.1%
112.86000061
< 0.1%
112.79000091
< 0.1%
112.76000211
< 0.1%
112.29000091
< 0.1%
112.27999881
< 0.1%
112.20999911
< 0.1%
111.44999691
< 0.1%
111.05000311
< 0.1%
2026-02-01T18:38:02.091490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

high
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct1999
Distinct (%)82.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.025371
Minimum27.48
Maximum114.83
Zeros0
Zeros (%)0.0%
Memory size37.9 KiB
2026-02-01T18:38:03.427487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum27.48
5-th percentile43.02
Q152.852499
median74.810001
Q394.707499
95-th percentile105.605
Maximum114.83
Range87.350002
Interquartile range (IQR)41.855

Descriptive statistics

Standard deviation22.126455
Coefficient of variation (CV)0.29890368
Kurtosis-1.3972899
Mean74.025371
Median Absolute Deviation (MAD)21.184998
Skewness-0.02961347
Sum179585.55
Variance489.58003
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.6207466623
2026-02-01T18:38:03.516295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-02-01T18:38:03.754820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps503
min3 days
max5 days
mean3 days, 3 hours and 11 minutes
std8 hours, 18 minutes and 9.91 seconds
2026-02-01T18:38:04.542527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
46.529998785
 
0.2%
94.639999394
 
0.2%
97.819999694
 
0.2%
48.200000764
 
0.2%
46.409999854
 
0.2%
53.430000314
 
0.2%
48.740001684
 
0.2%
52.779998784
 
0.2%
86.370002754
 
0.2%
92.879997254
 
0.2%
Other values (1989)2385
98.3%
ValueCountFrequency (%)
27.479999541
< 0.1%
28.579999921
< 0.1%
29.219999311
< 0.1%
29.659999851
< 0.1%
30.209999081
< 0.1%
30.251
< 0.1%
30.610000611
< 0.1%
30.729999541
< 0.1%
31.180000311
< 0.1%
31.379999161
< 0.1%
ValueCountFrequency (%)
114.83000181
< 0.1%
114.18000031
< 0.1%
113.97000121
< 0.1%
113.48000341
< 0.1%
113.45999911
< 0.1%
113.40000151
< 0.1%
113.22000121
< 0.1%
113.18000031
< 0.1%
112.63999941
< 0.1%
112.48000341
< 0.1%
2026-02-01T18:38:03.594441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

low
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct2034
Distinct (%)83.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.070932
Minimum26.049999
Maximum112.25
Zeros0
Zeros (%)0.0%
Memory size37.9 KiB
2026-02-01T18:38:05.002026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum26.049999
5-th percentile41.4475
Q151.0225
median72.57
Q392.944998
95-th percentile103.865
Maximum112.25
Range86.200001
Interquartile range (IQR)41.922498

Descriptive statistics

Standard deviation21.949091
Coefficient of variation (CV)0.30454846
Kurtosis-1.3790155
Mean72.070932
Median Absolute Deviation (MAD)20.879999
Skewness-0.027402992
Sum174844.08
Variance481.7626
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.5919736637
2026-02-01T18:38:05.086324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-02-01T18:38:05.273382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps503
min3 days
max5 days
mean3 days, 3 hours and 11 minutes
std8 hours, 18 minutes and 9.91 seconds
2026-02-01T18:38:06.014670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
96.260002144
 
0.2%
96.510002144
 
0.2%
92.860000614
 
0.2%
88.209999083
 
0.1%
96.370002753
 
0.1%
95.209999083
 
0.1%
82.290000923
 
0.1%
85.419998173
 
0.1%
95.339996343
 
0.1%
85.110000613
 
0.1%
Other values (2024)2393
98.6%
ValueCountFrequency (%)
26.049999241
< 0.1%
26.190000531
< 0.1%
26.950000761
< 0.1%
27.239999771
< 0.1%
27.739999771
< 0.1%
27.870000841
< 0.1%
28.209999081
< 0.1%
28.700000761
< 0.1%
28.729999541
< 0.1%
29.049999241
< 0.1%
ValueCountFrequency (%)
112.251
< 0.1%
111.69000241
< 0.1%
111.12000271
< 0.1%
111.08000181
< 0.1%
1111
< 0.1%
110.81999971
< 0.1%
110.70999911
< 0.1%
110.30000311
< 0.1%
110.11000061
< 0.1%
109.11000061
< 0.1%
2026-02-01T18:38:05.149354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

open
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct2011
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.099307
Minimum27.299999
Maximum113.89
Zeros0
Zeros (%)0.0%
Memory size37.9 KiB
2026-02-01T18:38:06.477670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum27.299999
5-th percentile42.075001
Q152.049999
median73.905003
Q393.887499
95-th percentile104.8
Maximum113.89
Range86.59
Interquartile range (IQR)41.8375

Descriptive statistics

Standard deviation22.063146
Coefficient of variation (CV)0.30182428
Kurtosis-1.3870611
Mean73.099307
Median Absolute Deviation (MAD)21.065002
Skewness-0.029825797
Sum177338.92
Variance486.78242
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.6313748916
2026-02-01T18:38:06.566456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-02-01T18:38:06.759765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps503
min3 days
max5 days
mean3 days, 3 hours and 11 minutes
std8 hours, 18 minutes and 9.91 seconds
2026-02-01T18:38:07.516005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
99.199996955
 
0.2%
93.489997865
 
0.2%
97.300003054
 
0.2%
49.310001374
 
0.2%
66.620002754
 
0.2%
95.790000924
 
0.2%
45.650001533
 
0.1%
82.440002443
 
0.1%
92.889999393
 
0.1%
99.760002143
 
0.1%
Other values (2001)2388
98.4%
ValueCountFrequency (%)
27.299999241
< 0.1%
27.340000151
< 0.1%
28.329999921
< 0.1%
28.350000381
< 0.1%
28.360000611
< 0.1%
29.079999921
< 0.1%
29.139999391
< 0.1%
29.200000761
< 0.1%
29.719999311
< 0.1%
29.751
< 0.1%
ValueCountFrequency (%)
113.88999941
< 0.1%
113.27999881
< 0.1%
113.12999731
< 0.1%
112.98000341
< 0.1%
112.81999971
< 0.1%
112.33999631
< 0.1%
112.15000151
< 0.1%
111.88999941
< 0.1%
111.37000271
< 0.1%
110.68000031
< 0.1%
2026-02-01T18:38:06.636735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

volume
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct2419
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean415995.67
Minimum48516
Maximum1311000
Zeros0
Zeros (%)0.0%
Memory size37.9 KiB
2026-02-01T18:38:07.986436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum48516
5-th percentile150813.5
Q1253342
median350919.5
Q3560700.5
95-th percentile806918
Maximum1311000
Range1262484
Interquartile range (IQR)307358.5

Descriptive statistics

Standard deviation212443.45
Coefficient of variation (CV)0.51068668
Kurtosis0.07966965
Mean415995.67
Median Absolute Deviation (MAD)129060.5
Skewness0.82276374
Sum1.0092055 × 109
Variance4.5132219 × 1010
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.2434870995
2026-02-01T18:38:08.082235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2026-02-01T18:38:08.368882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps503
min3 days
max5 days
mean3 days, 3 hours and 11 minutes
std8 hours, 18 minutes and 9.91 seconds
2026-02-01T18:38:09.248451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2263432
 
0.1%
3179972
 
0.1%
1324272
 
0.1%
8956432
 
0.1%
5988362
 
0.1%
2975222
 
0.1%
2500382
 
0.1%
4769881
 
< 0.1%
5688391
 
< 0.1%
5648461
 
< 0.1%
Other values (2409)2409
99.3%
ValueCountFrequency (%)
485161
< 0.1%
518771
< 0.1%
656661
< 0.1%
837021
< 0.1%
846271
< 0.1%
908241
< 0.1%
913981
< 0.1%
927801
< 0.1%
931301
< 0.1%
952701
< 0.1%
ValueCountFrequency (%)
13110001
< 0.1%
12535661
< 0.1%
11823271
< 0.1%
11735811
< 0.1%
11473891
< 0.1%
11362321
< 0.1%
11354401
< 0.1%
11249591
< 0.1%
11080521
< 0.1%
10956431
< 0.1%
2026-02-01T18:38:08.178629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

Interactions

2026-02-01T18:37:59.394299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:57.765816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:58.084011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:58.453619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:58.765434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:59.083215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:59.452707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:57.819770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:58.198253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:58.503011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:58.825354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:59.133571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:59.508234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:57.871454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:58.247853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:58.553402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:58.874471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:59.187356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:59.564299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:57.920615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:58.300093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:58.603252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:58.925064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:59.235771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:59.619137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:57.973783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:58.348259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:58.655668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:58.975337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:59.286567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:59.672829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:58.025663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:58.396625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:58.705883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:59.024656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-01T18:37:59.338497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-01T18:38:09.679119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
adj closeclosehighlowopenvolume
adj close1.0001.0000.9990.9990.997-0.601
close1.0001.0000.9990.9990.997-0.601
high0.9990.9991.0000.9990.999-0.595
low0.9990.9990.9991.0000.999-0.606
open0.9970.9970.9990.9991.000-0.599
volume-0.601-0.601-0.595-0.606-0.5991.000

Missing values

2026-02-01T18:37:59.754419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-01T18:37:59.806741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Dateadj closeclosehighlowopenvolume
2010-01-042010-01-0481.51000281.51000281.68000079.62999779.629997263542
2010-01-052010-01-0581.76999781.76999782.00000080.94999781.629997258887
2010-01-062010-01-0683.18000083.18000083.51999780.84999881.430000370059
2010-01-072010-01-0782.66000482.66000483.36000182.26000283.199997246632
2010-01-082010-01-0882.75000082.75000083.47000181.80000382.650002310377
2010-01-112010-01-1182.51999782.51999783.94999781.95999982.879997296304
2010-01-122010-01-1280.79000180.79000182.33999679.91000482.070000333866
2010-01-132010-01-1379.65000279.65000280.66999878.37000380.059998401627
2010-01-142010-01-1479.38999979.38999980.36000178.91999879.629997275404
2010-01-152010-01-1578.00000078.00000079.30999877.69999779.199997200555
Dateadj closeclosehighlowopenvolume
2019-08-132019-08-1357.09999857.09999857.47000154.20999954.730000783486
2019-08-142019-08-1455.23000055.23000056.84999853.97000156.740002718293
2019-08-152019-08-1554.47000154.47000155.33000253.77000054.900002527866
2019-08-162019-08-1654.86999954.86999955.66999854.25999854.740002168345
2019-08-192019-08-1956.20999956.20999956.41000054.84000054.959999113571
2019-08-202019-08-2056.34000056.34000056.59999855.27999956.099998659258
2019-08-212019-08-2155.68000055.68000057.13000155.54999956.049999704035
2019-08-222019-08-2255.34999855.34999856.45999954.84999855.939999621573
2019-08-232019-08-2354.16999854.16999855.59999853.24000255.349998807151
2019-08-262019-08-2653.63999953.63999955.25999852.95999953.250000679022